Information about Test

  1. EEG analysis

    aimlexchange.com/search/wiki/page/EEG_analysis

    Dynamical system Chaos theory Artificial neural network Deep learning Convolutional neural network Recurrent neural network Machine learning Artificial intelligence

  2. Sensor fusion

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    including: Central limit theorem Kalman filter Bayesian networks Dempster-Shafer Convolutional neural network Two example sensor fusion calculations are illustrated

  3. Conference on Neural Information Processing Systems

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    in 1986 as NIPS at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and

  4. SqueezeNet

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    Edgar (2017-03-02). "Introducing SqueezeDet: low power fully convolutional neural network framework for autonomous driving". The Intelligence of Information

  5. Jürgen Schmidhuber

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    his postdoc Dan Ciresan also achieved dramatic speedups of convolutional neural networks (CNNs) on fast parallel computers called GPUs. An earlier CNN

  6. Tsetlin machine

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    and more efficient primitives compared to more ordinary artificial neural networks, but while the method may be faster it has a steep drop in signal-to-noise

  7. Graphical model

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    Markov models, neural networks and newer models such as variable-order Markov models can be considered special cases of Bayesian networks. Naive Bayes classifier

  8. Bias–variance tradeoff

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    when increasing the width of a neural network. This means that it is not necessary to control the size of a neural network to control variance. This does

  9. Kernel method

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    (SVM) in the 1990s, when the SVM was found to be competitive with neural networks on tasks such as handwriting recognition. The kernel trick avoids the

  10. Bootstrap aggregating

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    procedures" (Breiman, 1996), which include, for example, artificial neural networks, classification and regression trees, and subset selection in linear

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